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This paper presents an inverse kinematics control algorithm based on RBF neural networks for manipulators. First, the initial RBF neural networks are trained off-line. The steepest descend method is used to on-line adjust conjunctive weights. A momentum term is used in the learning process. The learning rates are local adjusted for each term of conjunctive weight matrix in terms of variety of errors. The speed of learning has accelerated. The simulation experiments show this method has rapid convergence speed and high control accuracy.